• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.993-1001
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• 2000

This research features a method that quantitatively evaluates the performance of edge detection algorithms. Contrary to conventional methods that evaluate the performance of edge detection as a function of the amount of noise added to he input image, the proposed method is capable of assessing the performance of edge detection algorithms based on chosen parameters that influence the performance of edge detection. We have proposed a quantitative measure, called average performance index, that compares the average performance of different edge detection algorithms. We have applied the method to the commonly used edge detectors, Sobel, LOG(Laplacian of Gaussian), and Canny edge detectors for noisy images that contain straight line edges and curved line edges. Two kinds of noises i.e, Gaussian and impulse noises, are used. Experimental results show that our method of quantitatively evaluating the performance of edge detection algorithms can facilitate the selection of the optimal dge detection algorithm for a given task.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.9
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• pp.491-496
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• 2014

We derive an analytical expression for the bit-error rate (ber) of optimal single user detection (osud) of a binary phase-shift keying (bpsk) signal corrupted by a gaussian cochannel interferer (cci). the channel capacity is also calculated to investigate the ber performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.4
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• pp.284-290
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• 2013

Feature detection is very important to image processing area. In this paper we compare and analyze some characteristics of image processing algorithms for corner and blob feature detection. We also analyze the simulation results through image matching process. We show that how these algorithms work and how fast they execute. The simulation results are shown for helping us to select an algorithm or several algorithms extracting corner and blob feature.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.195-206
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• 2016

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data.

• Journal of the Optical Society of Korea
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• v.20 no.6
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• pp.745-751
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• 2016

A detection algorithm, based on the combined local-global (CLG) optical-flow model and Gaussian pyramid for a moving target appearing against a dynamic background, can compensate for the inadaptability of the classic Horn-Schunck algorithm to illumination changes and reduce the number of needed calculations. Incorporating the hypothesis of gradient conservation into the traditional CLG optical-flow model and combining structure and texture decomposition enable this algorithm to minimize the impact of illumination changes on optical-flow estimates. Further, calculating optical-flow with the Gaussian pyramid by layers and computing optical-flow at other points using an optical-flow iterative with higher gray-level points together reduce the number of calculations required to improve detection efficiency. Finally, this proposed method achieves the detection of a moving target against a dynamic background, according to the background motion vector determined by the displacement and magnitude of the optical-flow. Simulation results indicate that this algorithm, in comparison to the traditional Horn-Schunck optical-flow algorithm, accurately detects a moving target undergoing illumination changes against a dynamic background and simultaneously demonstrates a significant reduction in the number of computations needed to improve detection efficiency.

• ETRI Journal
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• v.41 no.4
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• pp.437-451
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• 2019

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph-based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state-of-the-art methods in terms of accuracy and work well with various data types.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.92-99
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• 2008

We propose an effective voice-pishing detection algorithm based on the 3GPP2 selectable mode vocoder (SMV). The detection of voice pishing is performed based on a Gaussian mixture model (GMM) using decoding parameters of the SMV directly extracted from the decoding process of the transmitted speech information in the mobile phone. The experimental results indicate that SMV decoding parameters are effective in discriminating between general voice and phisher's voice and the performance is significantly acceptable when the proposed technique is applied.

• Smart Media Journal
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• v.5 no.1
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• pp.78-87
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• 2016

The major problems of recent object tracking methods are related to the inefficient detection of moving objects due to occlusions, noisy background and inconsistent body motion. This paper presents a robust method for the detection and tracking of a moving in infrared animal videos. The tracking system is based on adaptive optical flow generation, Gaussian mixture and Kalman filtering. The adaptive Gaussian model of optical flow (GMOF) is used to extract foreground and noises are removed based on the object motion. Kalman filter enables the prediction of the object position in the presence of partial occlusions, and changes the size of the animal detected automatically along the image sequence. The presented method is evaluated in various environments of unstable background because of winds, and illuminations changes. The results show that our approach is more robust to background noises and performs better than previous methods.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.402-404
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• 2003

We present two methods for the automatic selection of the threshold values in unsupervised change detection. Both methods consist of the same two procedures: 1) to determine the parameters of Gaussian mixtures from a difference image or ratio image, 2) to determine threshold values using the Bayesian rule for minimum error. In the first method, the Expectation-Maximization algorithm is applied for estimating the parameters of the Gaussian mixtures. The second method is based on the iterative thresholding that successively employs thresholding and estimation of the model parameters. The effectiveness and applicability of the methods proposed here are illustrated by an experiment on the multi-temporal KOMPAT-1 EOC images.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1137-1142
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• 2013

FBG sensors are mainly used to measure strain and temperature of structures. In this paper, an interrogator of FBG sensors is developed and implemented to measure the crack of structures using FPGA and DSP. Developed interrogator consists of an optical source, an optical circulator, an optical grating and a CCD sensor and controller. The spectrum of the reflected light from the FBG sensor is analyzed and peak wavelength is detected. Next, strain of structure can be measured using shift of peak wavelength. Centroid algorithm and Gaussian fitting which are mainly applied to detect peak wavelength of the interrogator are compared in this paper. As a result of experiment, Gaussian fitting is suitable for a developed interrogator.